We propose NeuroLingua, a language - inspired framework that conceptualizes sleep as a structured physiological language. Each 30 - second epoch is decomposed into overlapping 3 - second subwindows ("tokens") using a CNN - based tokenizer, enabling hierarchical temporal modeling through dual - level Transformers: intra - segment encoding of local dependencies and inter - segment integration across seven consecutive epochs (3.5 minutes) for extended context. Modality - specific embeddings from EEG and EOG channels are fused via a Graph Convolutional Network, facilitating robust multimodal integration. NeuroLingua is evaluated on the Sleep - EDF Expanded and ISRUC - Sleep datasets, achieving state - of - the - art results on Sleep - EDF (85.3% accuracy, 0.800 macro F1, and 0.796 Cohen's κ), and competitive performance on ISRUC (81.9% accuracy, 0.802 macro F1, and 0.755 κ), matching or exceeding published baselines in overall and per - class metrics. The architecture's attentio n mechanisms enhance the detection of clinically relevant sleep microevents, providing a principled foundation for future interpretability, explainability and causal inference in sleep research. By framing sleep as a compositional language, NeuroLingua uni fies hierarchical sequence modeling and multimodal fusion, advancing automated sleep staging toward more transparent and clinically meaningful applications. Index Terms -- Sleep staging, EEG, EOG, Polysomnography, Deep learning, Hierarchical sequence modeling, Multimodal fusion, Transformers, Graph neural networks, Interpretability, Explainability, Causal inference.

In this study, we use persistent homology to perform classification tasks on two publicly available multivariate time series datasets [19, 11] that include physiological data collected during stressful and non stressful tasks. Instead of directly computing signal-specific features from sliding windows and subwindows on modalities such as electrocardiogram and wrist temperature (Figure 7), we extracted features using persistence diagrams and their statistical properties. Subwindowing method allowed us to reduce noise without incurring an extra computational cost. We then developed machine learning models and assess the performance of our models by varying window sizes and using different flavors of persistence diagrams. Topological Data Analysis (TDA) techniques usually work with points embedded in an affine space of large enough dimension. However, TDA techniques can still be applied to time series data sets whether they are univariate or multivariate. One can convert a univariate time series into a finite collection of points in a -dimensional affine space using delay embedding methods, of which one can compute persistent homology. Since Taken's Theorem implies that the delay embeddings produces topologically invariant subsets on a non-chaotical dynamical system [21], one can reasonably expect that persistent homology produces features that would distinguish different time series. There is a handful of research on the persistent homology of delay embeddings for time series classification [23, 20, 1].

It's one thing to want to protect endangered animals, but another entirely to keep track of them. Case in point: the dugong, a medium-sized marine mammal often referred to as a sea cow. Cute they may be, but spotting them in large bodies of water is easier said than done. Since marine researchers want to do so to keep tabs on population sizes, conservation status, and their important habitat areas, that poses a bit of a problem. Fortunately, this is where Dr. Amanda Hodgson of Australia's Murdoch University comes in.

It's one thing to want to protect endangered animals, but another entirely to keep track of them. Case in point: the dugong, a medium-sized marine mammal often referred to as a sea cow. Cute they may be, but spotting them in large bodies of water is easier said than done. Since marine researchers want to do so to keep tabs on population sizes, conservation status, and their important habitat areas, that poses a bit of a problem. Fortunately, this is where Dr. Amanda Hodgson of Australia's Murdoch University comes in.

It's one thing to want to protect endangered animals, but another entirely to keep track of them. Case in point: the dugong, a medium-sized marine mammal often referred to as a sea cow. Cute they may be, but spotting them in large bodies of water is easier said than done. Since marine researchers want to do so to keep tabs on population sizes, conservation status, and their important habitat areas, that poses a bit of a problem. Fortunately, this is where Dr. Amanda Hodgson of Australia's Murdoch University comes in.

Here novelty detection identifies salient image features to guide autonomous robotic exploration.  There is little advance knowledge of the features in the scene or the proportion that should count as outliers.  A new algorithm addresses this ambiguity by modeling novel data in advance and characterizing regular data at run time.  Detection thresholds adapt dynamically to reduce misclassification risk while accommodating homogeneous and heterogeneous scenes.  Experiments demonstrate the technique on a representative set of navigation images from the Mars Exploration Rover "Opportunity."  An efficient image analysis procedure filters each image using the integral transform.  Pixel-level features are aggregated into covariance descriptors that represent larger regions.  Finally, a distance metric derived from generalized eigenvalues permits novelty detection with kernel density estimation.  Results suggest that exploiting training examples of novel data can improve performance in this domain.